The present invention relates to methods and systems for automatically segmenting medical images. More specifically, it is related to image segmentation using a two phase approach of first modeling and then segmenting.
Automatic and semi-automatic segmentation algorithms have been proposed and studied in the literature extensively. Of specific interest herein is the model based approach. There are two major classes of model based approaches. In the first category, the model includes either shape or deformation of shape as for instance described in “Timothy F. Cootes, Christopher J. Taylor, David H. Cooper, Jim Graham: Active Shape Models-Their Training and Application. Computer Vision and Image Understanding 61(1): 38-59 (1995)” or appearance variations in a statistical framework as described in “Andrew Hill, Timothy F. Cootes, Christopher J. Taylor: Active Shape Models and the shape approximation problem. Image Vision Comput. 14(8): 601-607 (1996);” and “Timothy F. Cootes, Gareth J. Edwards, Christopher J. Taylor: Active Appearance Models. ECCV (2) 1998: 484-498.” In the second approach, a static template is used, where usually the global/local appearance variations in the form of histograms or probability density functions are modeled, as described in for instance “S. Warfield and A. Robatino and J. Dengler and F. Jolesz and R. Kikinis, Nonlinear Registration and Template Driven Segmentation, chapter 4, pp. 67-84, Progressive Publishing Alternatives, 1998.”
In both approaches, the model has to be initialized within the coordinate system of the data to be segmented. There are also numerous approaches as to which segmentation methodology is used and how the model is fit to the data in order to arrive at the final segmentation, as for instance described in “U. Grenander, General Pattern Theory. Oxford, U.K.: Oxford Univ. Press, 1994.”
Despite the large number of segmentation tasks in medical imaging, no universal method for producing an automatic segmentation has emerged. Instead, each segmentation task has been addressed (often several times) by a method which is specifically tailored for each segmentation problem. In contrast to these automatic segmentation approaches, the class of interactive segmentation methods is employed universally across segmentation tasks and modalities. Recently, these interactive methods have become quite mature and effective.
Currently, no universal methods and systems are available that automatically generate seeds for object to be segmented by learning a seed weight distribution, transferring automatically the seed weight distribution to the image data and invoking a seeded graph based segmentation. Accordingly, improved and novel methods and systems for such a seeded graph segmentation are required.